4DN DamID - Ki67 project - HCT116 Ki67-AID replication timing
Tom van Schaik
2021-10-11
Project
Mapping of Ki67 interactions with the genome and comparison with lamina interactions.
Introduction
Various analyses of HCT116 cells before and after Ki67 loss using a Ki67-AID degron system.
In this document, specifically replication timing.
Method
NA
Set-up
Set the parameters and list the data.
# Load dependencies - without warnings / messages
library(tidyverse)
library(GenomicRanges)
library(rtracklayer)
library(ggplot2)
library(RColorBrewer)
library(GGally)
library(corrr)
library(caTools)
# Prepare output
output_dir <- "ts210803_hct116_ki67_aid_replication_timing"
dir.create(output_dir, showWarnings = FALSE)
# Load input
chromosomes <- c(paste0("chr", 1:22), "chrX")
input_dir <- "ts210413_data_gathering"
bin_size <- readRDS(file.path(input_dir, "bin_size.rds"))
centromeres <- readRDS(file.path(input_dir, "centromeres.rds"))
colors_set1 <- readRDS(file.path(input_dir, "colors_set1.rds"))
colors_set2 <- readRDS(file.path(input_dir, "colors_set2.rds"))
colors_set3 <- readRDS(file.path(input_dir, "colors_set3.rds"))
tib_padamid_replicates <- readRDS(file.path(input_dir,
"tib_padamid_replicates.rds"))
tib_padamid_combined <- readRDS(file.path(input_dir,
"tib_padamid_combined.rds"))
gr_padamid_replicates <- readRDS(file.path(input_dir,
"gr_padamid_replicates.rds"))
gr_padamid_combined <- readRDS(file.path(input_dir,
"gr_padamid_combined.rds"))
tib_hmm_replicates <- readRDS(file.path(input_dir, "tib_hmm_replicates.rds"))
tib_hmm_combined <- readRDS(file.path(input_dir, "tib_hmm_combined.rds"))
padamid_metadata_replicates <- readRDS(file.path(input_dir,
"padamid_metadata_replicates.rds"))
padamid_metadata_combined <- readRDS(file.path(input_dir,
"padamid_metadata_combined.rds"))
# Prepare seqnames
chrom_sizes <- tibble(seqnames = seqlevels(gr_padamid_combined),
length = seqlengths(gr_padamid_combined)) %>%
arrange(-length)
# Scale pA-DamID scores?
tib_padamid_combined_scaled <- tib_padamid_combined %>%
mutate_at(4:ncol(.), function(x) scale(x)[, 1]) %>%
filter(seqnames != "chrY")library(knitr)
opts_chunk$set(fig.width = 10, fig.height = 4, cache = T,
dev=c('png', 'pdf'), fig.path = file.path(output_dir, "figures/"))
pdf.options(useDingbats = FALSE)# From Fede:
# ggpairs custom functions
corColor <- function(data, mapping, color = I("black"), sizeRange = c(1, 3), ...) {
x <- eval_data_col(data, mapping$x)
y <- eval_data_col(data, mapping$y)
r <- cor(x, y)
rt <- format(r, digits = 3)
tt <- as.character(rt)
cex <- max(sizeRange)
# helper function to calculate a useable size
percent_of_range <- function(percent, range) {
percent * diff(range) + min(range, na.rm = TRUE)
}
# plot correlation coefficient
p <- ggally_text(label = tt, mapping = aes(), xP = 0.5, yP = 0.5,
# size = I(percent_of_range(cex * abs(r), sizeRange)),
size = 6,
color = color, ...) +
theme(panel.grid.minor=element_blank(),
panel.grid.major=element_blank())
corColors <- RColorBrewer::brewer.pal(n = 7, name = "RdYlBu")[2:6]
if (r <= boundaries[1]) {
corCol <- corColors[1]
} else if (r <= boundaries[2]) {
corCol <- corColors[2]
} else if (r < boundaries[3]) {
corCol <- corColors[3]
} else if (r < boundaries[4]) {
corCol <- corColors[4]
} else {
corCol <- corColors[5]
}
p <- p +
theme(panel.background = element_rect(fill = corCol))
return(p)
}
customScatter <- function (data, mapping)
{
p <- ggplot(data = data, mapping) +
geom_bin2d(bins = 100) +
geom_smooth(method = "lm", se = T, col = "red") +
scale_fill_gradient(low = "lightgrey", high = "black", name = "Count") +
theme_bw()
p
}
PlotScatter <- function(tib, n1, n2, color_by = NULL, identity = F, smooth = 1) {
# Get tibble
tib <- tib %>%
dplyr::select(seqnames, matches(n1), matches(n2)) %>%
rename_all(~ c("seqnames", "n1", "n2"))
if (smooth > 1) {
tib <- tib %>%
group_by(seqnames) %>%
mutate(n1 = runmean(n1, k = smooth),
n2 = runmean(n2, k = smooth))
}
# Prepare color
if (! is.null(color_by)) {
tib <- tib %>%
add_column(color = color_by) %>%
drop_na()
alpha = 1
limits_color <- quantile(tib$color, c(0.001, 0.999), na.rm = T)
tib$color[tib$color < limits_color[1]] <- limits_color[1]
tib$color[tib$color > limits_color[2]] <- limits_color[2]
} else {
tib <- tib %>% drop_na()
tib$color = "1"
alpha = 0.02
}
# Plot
xlimits <- quantile(tib$n1, c(0.001, 0.999), na.rm = T) * 1.4
ylimits <- quantile(tib$n2, c(0.001, 0.999), na.rm = T) * 1.4
plt <- tib %>%
arrange(sample(1:nrow(.), size = nrow(.), replace = F)) %>%
ggplot(aes(x = n1, y = n2, color = color)) +
geom_point(size = 0.5, alpha = alpha) +
geom_hline(yintercept = 0, col = "black", linetype = "dashed") +
geom_vline(xintercept = 0, col = "black", linetype = "dashed") +
xlab(n1) +
ylab(n2) +
ggtitle(paste0("Spearman: ",
round(cor(tib$n1, tib$n2, use = "complete.obs",
method = "spearman"), 2))) +
coord_cartesian(xlim = xlimits, ylim = ylimits) +
theme_bw() +
theme(aspect.ratio = 1)
# Prepare color
if (! is.null(color_by)) {
plt <- plt +
scale_color_gradient2(low = "blue", mid = "grey", high = "red",
midpoint = 0)
} else {
plt <- plt +
scale_color_manual(values = "black", guide = "none")
}
if (identity) plt <- plt + geom_abline(slope = 1, intercept = 0, col = "red", linetype = "dashed")
plot(plt)
}
PlotDataTracksLight <- function(input_tib, exp_names, centromeres,
color_groups, plot_chr = "chr1",
plot_start = 1, plot_end = 500e6,
smooth = 1, color_list = NULL,
fix_yaxis = F) {
# Exp names is a vector of sample names
exp_search <- paste(exp_names, collapse = "|")
# Get the scores for the samples
tib_plot <- input_tib %>%
dplyr::select(seqnames, start, end,
all_of(exp_names))
if (smooth > 1) {
tib_plot <- tib_plot %>%
mutate_at(vars(contains("_")),
runmean, k = smooth)
}
# Filter for plotting window
tib_plot <- tib_plot %>%
filter(seqnames == plot_chr,
start >= plot_start,
end <= plot_end)
# Gather
tib_gather <- tib_plot %>%
gather(key, value, -seqnames, -start, -end) %>%
mutate(key = factor(key, levels = exp_names),
fill_column = color_groups[match(key, names(color_groups))],
fill_column = factor(fill_column, levels = unique(color_groups)))
# Centromeres
centromeres.plt <- as_tibble(centromeres) %>%
filter(seqnames == plot_chr) %>%
gather(key_centromeres, value, start, end)
# Plot
ylimits <- quantile(tib_gather$value, c(0.001, 0.999), na.rm = T)
fill_column <- NULL
plt <- tib_gather %>%
ggplot(aes(fill = fill_column))
# Set all counts tracks to the same limits
plt <- plt +
geom_rect(aes(xmin = start / 1e6, xmax = end / 1e6,
ymin = 0, ymax = value)) +
geom_line(data = centromeres.plt,
aes(x = value / 1e6, y = 0),
color = "black", size = 2) +
geom_hline(yintercept = 0, size = 0.5) +
facet_grid(key ~ ., scales = "free_y") +
xlab(paste0(plot_chr, " (Mb)")) +
ylab("Score") +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0.025, 0.025)) +
theme_classic()
if (! is.null(color_list)) {
colors <- levels(tib_gather$fill_column)
color_list <- color_list[1:length(colors)]
names(color_list) <- colors
#colors <- recode(colors, !!!color_list)
plt <- plt +
scale_fill_manual(values = color_list, guide = "none")
} else {
plt <- plt +
scale_fill_brewer(palette = "Set1", guide = "none")
}
if (fix_yaxis) {
plt <- plt +
coord_cartesian(xlim = c(max(c(plot_start,
min(tib_gather$start))) / 1e6,
min(c(plot_end,
max(tib_gather$end))) / 1e6),
ylim = ylimits)
} else {
plt <- plt +
coord_cartesian(xlim = c(max(c(plot_start,
min(tib_gather$start))) / 1e6,
min(c(plot_end,
max(tib_gather$end))) / 1e6))
}
plot(plt)
}
PlotScatterBinned <- function(tib, n1, n2, color_by = NULL, identity = F,
n_min = 10, ylimits_col = c(-2.4, 2.4),
count_range = c(0, 400), smooth = 1,
remove_outliers = F, midpoint_col = 0,
linear_gradient = F) {
# Get tibble
tib_process <- tib %>%
dplyr::select(seqnames, all_of(n1), all_of(n2)) %>%
rename_all(~ c("seqnames", "n1", "n2"))
# Calculate pearson correlation without any smoothing
tib_cor <- round(cor(tib_process$n1, tib_process$n2, use = "complete.obs",
method = "pearson"), 2)
if (smooth > 1) {
tib_process <- tib_process %>%
group_by(seqnames) %>%
mutate(n1 = runmean(n1, k = smooth),
n2 = runmean(n2, k = smooth))
}
if (! is.null(color_by)) {
tib_process <- tib_process %>%
add_column(color = color_by)
}
tib_process <- tib_process %>%
drop_na()
# Change color range
if (! is.null(color_by)) {
limits_color <- quantile(tib_process$color, c(0.001, 0.999), na.rm = T)
tib_process$color[tib_process$color < limits_color[1]] <- limits_color[1]
tib_process$color[tib_process$color > limits_color[2]] <- limits_color[2]
}
# Replace outliers
if (remove_outliers) {
tib_process <- tib_process %>%
mutate(n1 = case_when(n1 > quantile(n1, 0.999) ~ quantile(n1, 0.999),
n1 < quantile(n1, 0.001) ~ quantile(n1, 0.001),
T ~ n1),
n2 = case_when(n2 > quantile(n2, 0.999) ~ quantile(n2, 0.999),
n2 < quantile(n2, 0.001) ~ quantile(n2, 0.001),
T ~ n2))
}
# Metrics
n1_min = min(tib_process$n1) - 0.001
n1_max = max(tib_process$n1) + 0.001
n1_binsize <- (n1_max - n1_min) / 49
n2_min = min(tib_process$n2) - 0.001
n2_max = max(tib_process$n2) + 0.001
n2_binsize <- (n2_max - n2_min) / 49
tib_summary <- tib_process %>%
mutate(n1_cut = cut(n1,
seq(n1_min, n1_max, length.out = 50)),
n2_cut = cut(n2,
seq(n2_min, n2_max, length.out = 50))) %>%
mutate(n1_bin = as.numeric(as.factor(n1_cut)),
n2_bin = as.numeric(as.factor(n2_cut))) %>%
mutate(n1_bin = n1_min - n1_binsize/2 + n1_bin * n1_binsize,
n2_bin = n2_min - n2_binsize/2 + n2_bin * n2_binsize) %>%
group_by(n1_bin, n2_bin)
# Plot
if (! is.null(color_by)) {
tib_summary <- tib_summary %>%
dplyr::summarise(n = n(),
mark = mean(color)) %>%
ungroup() %>%
filter(n >= n_min)
plt <- tib_summary %>%
ggplot(aes(x = n1_bin, y = n2_bin)) +
geom_tile(aes(fill = mark))
if (linear_gradient) {
plt <- plt +
scale_fill_gradient(low = "darkred", high = "grey80",
limits = ylimits_col,
na.value = "green")
} else {
plt <- plt +
scale_fill_gradient2(low = "blue", mid = "grey", high = "red",
midpoint = midpoint_col, limits = ylimits_col,
na.value = "green")
}
} else {
tib_summary <- tib_summary %>%
dplyr::summarise(n = n()) %>%
ungroup() %>%
filter(n >= n_min)
plt <- tib_summary %>%
ggplot(aes(x = n1_bin, y = n2_bin)) +
geom_tile(aes(fill = n)) +
scale_fill_gradient(low = "lightgrey", high = "black", name = "Count",
limits = count_range, na.value = "green")
}
plt <- plt +
geom_hline(yintercept = 0, linetype = "dashed", col = "black") +
geom_vline(xintercept = 0, linetype = "dashed", col = "black") +
xlab(n1) +
ylab(n2) +
ggtitle(paste0("Pearson: ", tib_cor)) +
theme_bw() +
theme(aspect.ratio = 1)
if (identity) plt <- plt + geom_abline(slope = 1, intercept = 0,
col = "black", linetype = "dashed")
plot(plt)
}
PlotDataTracksLightFaceted <- function(input_tib, exp_names, centromeres,
color_groups, facet_groups,
plot_chr = "chr1",
plot_start = 1, plot_end = 500e6,
smooth = 1, color_list = NULL,
fix_yaxis = F, remove_NA_bins = F) {
# Exp names is a vector of sample names
exp_search <- paste(exp_names, collapse = "|")
# Get the scores for the samples
tib_plot <- input_tib %>%
dplyr::select(seqnames, start, end,
matches(exp_search))
if (smooth > 1) {
tib_plot <- tib_plot %>%
mutate_at(vars(contains("_")),
runmean, k = smooth)
}
# Remove NA bins
if (remove_NA_bins) {
idx_na <- rowMeans(is.na(tib_plot[, 4:ncol(tib_plot)])) > 0
tib_plot[idx_na, 4:ncol(tib_plot)] <- NA
}
# Filter for plotting window
tib_plot <- tib_plot %>%
filter(seqnames == plot_chr,
start >= plot_start,
end <= plot_end)
# Gather
tib_gather <- tib_plot %>%
gather(key, value, -seqnames, -start, -end) %>%
mutate(key = factor(key, levels = exp_names),
fill_column = color_groups[match(key, names(color_groups))],
fill_column = factor(fill_column, levels = unique(color_groups)),
facet_column = facet_groups[match(key, names(facet_groups))],
facet_column = factor(facet_column, levels = unique(facet_groups)))
# Centromeres
centromeres.plt <- as_tibble(centromeres) %>%
filter(seqnames == plot_chr) %>%
gather(key_centromeres, value, start, end)
# Plot
ylimits <- quantile(tib_gather$value, c(0.001, 0.999), na.rm = T)
fill_column <- NULL
plt <- tib_gather %>%
ggplot(aes(fill = fill_column))
# Set all counts tracks to the same limits
plt <- plt +
#geom_rect(aes(xmin = start / 1e6, xmax = end / 1e6,
# ymin = 0, ymax = value)) +
geom_line(aes(x = (start+end)/2e6, y = value,
col = fill_column)) +
geom_line(data = centromeres.plt,
aes(x = value / 1e6, y = 0),
color = "black", size = 2) +
geom_hline(yintercept = 0, size = 0.5) +
facet_grid(facet_column ~ ., scales = "free_y") +
xlab(paste0(plot_chr, " (Mb)")) +
ylab("Score") +
scale_x_continuous(expand = c(0, 0)) +
scale_y_continuous(expand = c(0.025, 0.025)) +
theme_classic()
if (! is.null(color_list)) {
colors <- levels(tib_gather$fill_column)
color_list <- color_list[1:length(colors)]
names(color_list) <- colors
#colors <- recode(colors, !!!color_list)
plt <- plt +
scale_fill_manual(values = color_list, guide = "none") +
scale_color_manual(values = color_list, guide = "none")
} else {
plt <- plt +
scale_fill_brewer(palette = "Set1", guide = "none") +
scale_color_brewer(palette = "Set1", guide = "none")
}
if (fix_yaxis) {
plt <- plt +
coord_cartesian(xlim = c(max(c(plot_start,
min(tib_gather$start))) / 1e6,
min(c(plot_end,
max(tib_gather$end))) / 1e6),
ylim = ylimits)
} else {
plt <- plt +
coord_cartesian(xlim = c(max(c(plot_start,
min(tib_gather$start))) / 1e6,
min(c(plot_end,
max(tib_gather$end))) / 1e6))
}
plot(plt)
}1. Prepare data
Determine differences, and load replication timing data.
# Get metadata
padamid_metadata_combined_aid <- padamid_metadata_combined %>%
filter(experiment == "Ki67_aid") %>%
filter(! grepl("control", condition),
! grepl("thymidine", condition))
# Get tibble
tib <- tib_padamid_combined_scaled %>%
mutate(# RO vs double-thy
RO_vs_doublethy_Ki67 = HCT116_AID_RO_Ki67 - HCT116_AID_doublethy_Ki67,
RO_vs_doublethy_LMNB1 = HCT116_AID_RO_LMNB1 - HCT116_AID_doublethy_LMNB1,
RO_vs_doublethy_H3K27me3 = HCT116_AID_RO_H3K27me3 - HCT116_AID_doublethy_H3K27me3,
RO_vs_doublethy_H3K9me3 = HCT116_AID_RO_H3K9me3 - HCT116_AID_doublethy_H3K9me3,
# IAA vs nonIAA, double-thy
doublethy_LMNB1_diff = HCT116_AID_doublethy_IAA_LMNB1 - HCT116_AID_doublethy_LMNB1,
doublethy_H3K27me3_diff = HCT116_AID_doublethy_IAA_H3K27me3 - HCT116_AID_doublethy_H3K27me3,
doublethy_H3K9me3_diff = HCT116_AID_doublethy_IAA_H3K9me3 - HCT116_AID_doublethy_H3K9me3,
# IAA vs nonIAA, RO
RO_LMNB1_diff = HCT116_AID_RO_IAA_LMNB1 - HCT116_AID_RO_LMNB1,
RO_H3K27me3_diff = HCT116_AID_RO_IAA_H3K27me3 - HCT116_AID_RO_H3K27me3,
RO_H3K9me3_diff = HCT116_AID_RO_IAA_H3K9me3 - HCT116_AID_RO_H3K9me3)
# Smoothing of differences?
smooth_differences = F
if (smooth_differences) {
tib <- tib %>%
group_by(seqnames) %>%
mutate_at(vars(contains("vs"), contains("diff")),
function(x) runmean(x, k = 3)) %>%
ungroup()
}ts210804 - using replication timing data mapped by myself, rather than the data mapped by Ethan.
# Read replication timing
input_dir <- "../ts210607_repliseq_K562_LaminKO_HCT116_Ki67AID/ts210802_further_processing_replication_timing/"
rt_metadata <- readRDS(file.path(input_dir,
"tib_repliseq_metadata.rds"))
tib_rt <- readRDS(file.path(input_dir,
"tib_norm_average.rds")) %>%
rename_at(4:5, ~ c("minus_IAA", "plus_IAA")) %>%
mutate(rt_diff = plus_IAA - minus_IAA)
# Add to tibble
tib <- left_join(tib, tib_rt)## Joining, by = c("seqnames", "start", "end")# Add distance to centromeres
dis <- distanceToNearest(as(tib, "GRanges"), centromeres)
tib$distance_to_centromere <- mcols(dis)$distance / 1e6# Simple data tracks - for double-thy & RO
PlotDataTracksLight(input_tib = tib,
exp_names = c("HCT116_AID_doublethy_Ki67",
"HCT116_AID_doublethy_LMNB1",
"HCT116_AID_doublethy_IAA_LMNB1",
"minus_IAA",
"plus_IAA"),
centromeres,
color_groups = c(HCT116_AID_doublethy_Ki67 = "Ki67",
HCT116_AID_doublethy_LMNB1 = "LMNB1",
HCT116_AID_doublethy_IAA_LMNB1 = "LMNB1",
minus_IAA = "rt",
plus_IAA = "rt"),
smooth = 9, plot_chr = "chr3", fix_yaxis = T,
plot_start = 25e6, plot_end = 100e6,
color_list = colors_set1[c(1:5, 7)])## Warning: Removed 116 rows containing missing values (geom_rect).
PlotDataTracksLight(input_tib = tib,
exp_names = c("HCT116_AID_doublethy_Ki67",
"HCT116_AID_doublethy_LMNB1",
"HCT116_AID_doublethy_IAA_LMNB1",
"minus_IAA",
"plus_IAA"),
centromeres,
color_groups = c(HCT116_AID_doublethy_Ki67 = "Ki67",
HCT116_AID_doublethy_LMNB1 = "LMNB1",
HCT116_AID_doublethy_IAA_LMNB1 = "LMNB1",
minus_IAA = "rt",
plus_IAA = "rt"),
smooth = 9, plot_chr = "chr11", fix_yaxis = T,
plot_start = 25e6, plot_end = 100e6,
color_list = colors_set1[c(1:5, 7)])## Warning: Removed 187 rows containing missing values (geom_rect).
# Faceted plots
PlotDataTracksLightFaceted(input_tib = tib,
exp_names = c("HCT116_AID_doublethy_Ki67",
"HCT116_AID_doublethy_LMNB1",
"HCT116_AID_doublethy_IAA_LMNB1",
"minus_IAA",
"plus_IAA"),
centromeres,
color_groups = c(HCT116_AID_doublethy_Ki67 = "minusIAA",
HCT116_AID_doublethy_LMNB1 = "minusIAA",
HCT116_AID_doublethy_IAA_LMNB1 = "plusIAA",
minus_IAA = "minusIAA",
plus_IAA = "plusIAA"),
smooth = 9, plot_chr = "chr3", fix_yaxis = F,
plot_start = 60e6, plot_end = 110e6,
color_list = c("grey20", "red"),
facet_groups = c(HCT116_AID_doublethy_Ki67 = "Ki67",
HCT116_AID_doublethy_LMNB1 = "LMNB1",
HCT116_AID_doublethy_IAA_LMNB1 = "LMNB1",
minus_IAA = "rt",
plus_IAA = "rt"),
remove_NA_bins = T)
PlotDataTracksLightFaceted(input_tib = tib,
exp_names = c("HCT116_AID_doublethy_Ki67",
"HCT116_AID_doublethy_LMNB1",
"HCT116_AID_doublethy_IAA_LMNB1",
"minus_IAA",
"plus_IAA"),
centromeres,
color_groups = c(HCT116_AID_doublethy_Ki67 = "minusIAA",
HCT116_AID_doublethy_LMNB1 = "minusIAA",
HCT116_AID_doublethy_IAA_LMNB1 = "plusIAA",
minus_IAA = "minusIAA",
plus_IAA = "plusIAA"),
smooth = 9, plot_chr = "chr15", fix_yaxis = F,
plot_start = 18e6, plot_end = 91e6,
color_list = c("grey20", "red"),
facet_groups = c(HCT116_AID_doublethy_Ki67 = "Ki67",
HCT116_AID_doublethy_LMNB1 = "LMNB1",
HCT116_AID_doublethy_IAA_LMNB1 = "LMNB1",
minus_IAA = "rt",
plus_IAA = "rt"),
remove_NA_bins = T)## Warning: Removed 64 row(s) containing missing values (geom_path).
PlotDataTracksLightFaceted(input_tib = tib,
exp_names = c("HCT116_AID_doublethy_Ki67",
"HCT116_AID_doublethy_LMNB1",
"HCT116_AID_doublethy_IAA_LMNB1",
"minus_IAA",
"plus_IAA"),
centromeres,
color_groups = c(HCT116_AID_doublethy_Ki67 = "minusIAA",
HCT116_AID_doublethy_LMNB1 = "minusIAA",
HCT116_AID_doublethy_IAA_LMNB1 = "plusIAA",
minus_IAA = "minusIAA",
plus_IAA = "plusIAA"),
smooth = 9, plot_chr = "chr12", fix_yaxis = F,
plot_start = 11e6, plot_end = 48e6,
color_list = c("grey20", "red"),
facet_groups = c(HCT116_AID_doublethy_Ki67 = "Ki67",
HCT116_AID_doublethy_LMNB1 = "LMNB1",
HCT116_AID_doublethy_IAA_LMNB1 = "LMNB1",
minus_IAA = "rt",
plus_IAA = "rt"),
remove_NA_bins = T)
PlotDataTracksLightFaceted(input_tib = tib,
exp_names = c("HCT116_AID_doublethy_Ki67",
"HCT116_AID_doublethy_LMNB1",
"HCT116_AID_doublethy_IAA_LMNB1",
"minus_IAA",
"plus_IAA"),
centromeres,
color_groups = c(HCT116_AID_doublethy_Ki67 = "minusIAA",
HCT116_AID_doublethy_LMNB1 = "minusIAA",
HCT116_AID_doublethy_IAA_LMNB1 = "plusIAA",
minus_IAA = "minusIAA",
plus_IAA = "plusIAA"),
smooth = 9, plot_chr = "chr12", fix_yaxis = F,
plot_start = 21e6, plot_end = 45e6,
color_list = c("grey20", "red"),
facet_groups = c(HCT116_AID_doublethy_Ki67 = "Ki67",
HCT116_AID_doublethy_LMNB1 = "LMNB1",
HCT116_AID_doublethy_IAA_LMNB1 = "LMNB1",
minus_IAA = "rt",
plus_IAA = "rt"),
remove_NA_bins = T)
PlotDataTracksLightFaceted(input_tib = tib,
exp_names = c("HCT116_AID_doublethy_Ki67",
"HCT116_AID_doublethy_LMNB1",
"HCT116_AID_doublethy_IAA_LMNB1",
"minus_IAA",
"plus_IAA"),
centromeres,
color_groups = c(HCT116_AID_doublethy_Ki67 = "minusIAA",
HCT116_AID_doublethy_LMNB1 = "minusIAA",
HCT116_AID_doublethy_IAA_LMNB1 = "plusIAA",
minus_IAA = "minusIAA",
plus_IAA = "plusIAA"),
smooth = 9, plot_chr = "chr12", fix_yaxis = F,
plot_start = 11e6, plot_end = 48e6,
color_list = c("grey20", "red"),
facet_groups = c(HCT116_AID_doublethy_Ki67 = "Ki67",
HCT116_AID_doublethy_LMNB1 = "LMNB1",
HCT116_AID_doublethy_IAA_LMNB1 = "LMNB1",
minus_IAA = "rt",
plus_IAA = "rt"),
remove_NA_bins = F)
PlotDataTracksLightFaceted(input_tib = tib,
exp_names = c("HCT116_AID_doublethy_Ki67",
"HCT116_AID_doublethy_LMNB1",
"HCT116_AID_doublethy_IAA_LMNB1",
"minus_IAA",
"plus_IAA"),
centromeres,
color_groups = c(HCT116_AID_doublethy_Ki67 = "minusIAA",
HCT116_AID_doublethy_LMNB1 = "minusIAA",
HCT116_AID_doublethy_IAA_LMNB1 = "plusIAA",
minus_IAA = "minusIAA",
plus_IAA = "plusIAA"),
smooth = 9, plot_chr = "chr22", fix_yaxis = F,
#plot_start = 20e6, plot_end = 91e6,
color_list = c("grey20", "red"),
facet_groups = c(HCT116_AID_doublethy_Ki67 = "Ki67",
HCT116_AID_doublethy_LMNB1 = "LMNB1",
HCT116_AID_doublethy_IAA_LMNB1 = "LMNB1",
minus_IAA = "rt",
plus_IAA = "rt"),
remove_NA_bins = T)
for (chr in chromosomes) {
# doublethy
PlotDataTracksLight(input_tib = tib,
exp_names = c("HCT116_AID_doublethy_Ki67",
"HCT116_AID_doublethy_LMNB1",
"doublethy_LMNB1_diff",
"minus_IAA",
"rt_diff"),
centromeres,
color_groups = c(HCT116_AID_doublethy_Ki67 = "Ki67",
HCT116_AID_doublethy_LMNB1 = "LMNB1",
doublethy_LMNB1_diff = "LMNB1",
minus_IAA = "rt",
rt_diff = "rt"),
smooth = 15, plot_chr = chr, fix_yaxis = F,
color_list = c(colors_set1[c(1:2)], "grey20"))
}## Warning: Removed 1734 rows containing missing values (geom_rect).
## Warning: Removed 109 rows containing missing values (geom_rect).
## Warning: Removed 87 rows containing missing values (geom_rect).
## Warning: Removed 87 rows containing missing values (geom_rect).
## Warning: Removed 178 rows containing missing values (geom_rect).
## Warning: Removed 63 rows containing missing values (geom_rect).
## Warning: Removed 123 rows containing missing values (geom_rect).
## Warning: Removed 63 rows containing missing values (geom_rect).
## Warning: Removed 1570 rows containing missing values (geom_rect).
## Warning: Removed 87 rows containing missing values (geom_rect).
## Warning: Removed 173 rows containing missing values (geom_rect).
## Warning: Removed 111 rows containing missing values (geom_rect).
## Warning: Removed 1592 rows containing missing values (geom_rect).
## Warning: Removed 1665 rows containing missing values (geom_rect).
## Warning: Removed 1765 rows containing missing values (geom_rect).
## Warning: Removed 852 rows containing missing values (geom_rect).
## Warning: Removed 192 rows containing missing values (geom_rect).
## Warning: Removed 287 rows containing missing values (geom_rect).
## Warning: Removed 178 rows containing missing values (geom_rect).
## Warning: Removed 87 rows containing missing values (geom_rect).
## Warning: Removed 596 rows containing missing values (geom_rect).
## Warning: Removed 1160 rows containing missing values (geom_rect).
## Warning: Removed 278 rows containing missing values (geom_rect).
2. Simple correlations
# Scatter plot as before
tib_dropna <- tib %>% drop_na(HCT116_AID_doublethy_Ki67)
PlotScatterBinned(tib_dropna, smooth = 3, identity = T,
n1 = "minus_IAA",
n2 = "plus_IAA", count_range = c(0, 1500))## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib_dropna, smooth = 3, identity = T,
n1 = "minus_IAA",
n2 = "plus_IAA",
color_by = tib_dropna$HCT116_AID_doublethy_Ki67, ylimits_col = c(-2.8, 2.8))## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib_dropna, smooth = 3, identity = T,
n1 = "minus_IAA",
n2 = "plus_IAA",
color_by = tib_dropna$distance_to_centromere, ylimits_col = c(0, 80),
midpoint_col = 40)## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib_dropna, smooth = 3, identity = T,
n1 = "minus_IAA",
n2 = "plus_IAA",
color_by = log10(tib_dropna$distance_to_centromere+0.001),
ylimits_col = c(-1, 2.4), midpoint_col = 0.7)## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib_dropna, smooth = 3, identity = T,
n1 = "minus_IAA",
n2 = "plus_IAA",
color_by = log10(tib_dropna$distance_to_centromere+1),
ylimits_col = c(0, 2.4), midpoint_col = 1.2)## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib_dropna, smooth = 3,
n1 = "distance_to_centromere",
n2 = "rt_diff")## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
# Correlation as before
tib_dropna %>%
# Smoothing of 3 bins?
mutate_at(vars(contains("vs"), contains("diff")),
function(x) runmean(x, k = 3)) %>%
dplyr::summarise(cor_rt = cor(HCT116_AID_doublethy_Ki67, rt_diff,
use = "complete.obs"))## # A tibble: 1 × 1
## cor_rt
## <dbl>
## 1 0.0838# Plot chromosomal differences
tib_dropna %>%
group_by(seqnames) %>%
dplyr::summarise(rt = mean(rt_diff, na.rm = T)) %>%
ungroup() %>%
mutate(seqnames = factor(seqnames, chrom_sizes$seqnames),
rdna = seqnames %in% paste0("chr", c(13:15, 21:22))) %>%
ggplot(aes(x = seqnames, y = rt, col = rdna, shape = rdna)) +
geom_hline(yintercept = 0, col = "black", linetype = "dashed") +
geom_point(size = 2) +
ylab("Difference replication timing") +
scale_color_manual(values = c("grey20", "red")) +
theme_bw() +
theme(aspect.ratio = 1,
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
3. Centromeres
# Summary per chromosome
tib_summary <- tib_dropna %>%
filter(distance_to_centromere < 2) %>%
gather(key, value, contains("rt_diff")) %>%
group_by(seqnames, key) %>%
summarise(mean = mean(value, na.rm = T)) %>%
ungroup() %>%
mutate(seqnames = factor(seqnames, chrom_sizes$seqnames),
rdna = seqnames %in% paste0("chr", c(13, 14, 15, 21, 22))) %>%
mutate(size = chrom_sizes$length[match(seqnames,
chrom_sizes$seqnames)],
size = size / 1e6)## `summarise()` has grouped output by 'seqnames'. You can override using the `.groups` argument.# Plot
tib_summary %>%
ggplot(aes(x = seqnames, y = mean, col = rdna, shape = rdna)) +
geom_point(size = 2) +
geom_hline(yintercept = 0, col = "black") +
xlab("") +
ylab("Replication timing difference near centromeres") +
scale_color_manual(values = c("grey20", "red")) +
theme_bw() +
theme(aspect.ratio = 1,
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
# Make boxplots
tib_boxplots <- tib_dropna %>%
mutate(rdna = seqnames %in% paste0("chr", c(13:15, 21:22))) %>%
dplyr::select(seqnames, rdna, contains("distance"), rt_diff) %>%
mutate(dis_group = as.numeric(cut(distance_to_centromere,
breaks = seq(0, max(distance_to_centromere) + 1,
by = 0.5)))/2 - 0.5)
# Plot as boxplots
# First, calculate boxplots to have more control over ylimits
tib_summary <- tib_boxplots %>%
#group_by(seqnames, rdna, dis_group) %>%
group_by(dis_group) %>%
drop_na() %>%
summarise(ymin = quantile(rt_diff, 0.05),
lower = quantile(rt_diff, 0.25),
middle = quantile(rt_diff, 0.5),
upper = quantile(rt_diff, 0.75),
ymax = quantile(rt_diff, 0.95))
tib_summary %>%
filter(dis_group <= 20) %>%
ggplot(aes(x = dis_group, ymin = ymin, lower = lower, middle = middle,
upper = upper, ymax = ymax, group = dis_group, y = middle)) +
geom_boxplot(stat="identity", outlier.shape = NA, fill = "lightgrey", col = "black") +
geom_line(aes(y = middle, group = NULL), col = "red") +
geom_hline(yintercept = 0, col = "blue", linetype = "dashed") +
#facet_grid(. ~ seqnames, scales = "free_y") +
xlab("Distance to centromere (Mb)") +
ylab("Difference in RT") +
#coord_cartesian(xlim = c(0, 30)) +
theme_bw() +
theme(aspect.ratio = 1)
# Repeat, for LaminB1 interactions
tib_summary <- tib_dropna %>%
filter(distance_to_centromere < 2) %>%
gather(key, value, contains("doublethy_LMNB1_diff")) %>%
group_by(seqnames, key) %>%
summarise(mean = mean(value, na.rm = T)) %>%
ungroup() %>%
mutate(seqnames = factor(seqnames, chrom_sizes$seqnames),
rdna = seqnames %in% paste0("chr", c(13, 14, 15, 21, 22))) %>%
mutate(size = chrom_sizes$length[match(seqnames,
chrom_sizes$seqnames)],
size = size / 1e6)## `summarise()` has grouped output by 'seqnames'. You can override using the `.groups` argument.# Plot
tib_summary %>%
ggplot(aes(x = seqnames, y = mean, col = rdna, shape = rdna)) +
geom_point(size = 2) +
geom_hline(yintercept = 0, col = "black") +
xlab("") +
ylab("Lamin B1 difference near centromeres") +
scale_color_manual(values = c("grey20", "red")) +
theme_bw() +
theme(aspect.ratio = 1,
axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))
tib_boxplots <- tib_dropna %>%
mutate(rdna = seqnames %in% paste0("chr", c(13:15, 21:22))) %>%
dplyr::select(seqnames, rdna, contains("distance"), doublethy_LMNB1_diff) %>%
mutate(dis_group = as.numeric(cut(distance_to_centromere,
breaks = seq(0, max(distance_to_centromere) + 1,
by = 0.5)))/2 - 0.5)
# Plot as boxplots
# First, calculate boxplots to have more control over ylimits
tib_summary <- tib_boxplots %>%
#group_by(seqnames, rdna, dis_group) %>%
group_by(dis_group) %>%
drop_na() %>%
summarise(ymin = quantile(doublethy_LMNB1_diff, 0.05),
lower = quantile(doublethy_LMNB1_diff, 0.25),
middle = quantile(doublethy_LMNB1_diff, 0.5),
upper = quantile(doublethy_LMNB1_diff, 0.75),
ymax = quantile(doublethy_LMNB1_diff, 0.95))
tib_summary %>%
filter(dis_group <= 20) %>%
ggplot(aes(x = dis_group, ymin = ymin, lower = lower, middle = middle,
upper = upper, ymax = ymax, group = dis_group, y = middle)) +
geom_boxplot(stat="identity", outlier.shape = NA, fill = "lightgrey", col = "black") +
geom_line(aes(y = middle, group = NULL), col = "red") +
geom_hline(yintercept = 0, col = "blue", linetype = "dashed") +
#facet_grid(. ~ seqnames, scales = "free_y") +
xlab("Distance to centromere (Mb)") +
ylab("Difference in Lamin B1") +
#coord_cartesian(xlim = c(0, 30)) +
theme_bw() +
theme(aspect.ratio = 1)
4. Replication timing vs Ki67 scores
I also want to include a more basic figure - how does Ki67 correlate with replication timing? And how does this compare with LaminB1?
# Make simple scatter plots first
PlotScatterBinned(tib, "HCT116_AID_doublethy_Ki67", "minus_IAA")## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib, "HCT116_AID_doublethy_LMNB1", "minus_IAA")## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib, "HCT116_AID_doublethy_Ki67", "HCT116_AID_doublethy_LMNB1",
color_by = tib$minus_IAA, ylimits_col = c(-4.5, 4.5))## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
I also want to make this figure in the wildtype cells - can I do that?
# Load replication from wt cells
# Read RDS file
repliseq <- readRDS("../ts191220_laminaVsNucleolus_NewAnalyses/ts200116_ProcessRepliseq/GRanges_repliseq_mean.rds")
# # Invert to L/E?
# mcols(repliseq) <- as_tibble(mcols(repliseq)) %>%
# mutate_all(function(x) -x)
# Find overlaps with DamID bins
gr_repliseq <- as(tib_padamid_combined, "GRanges")
ovl <- findOverlaps(gr_repliseq, repliseq, select = "arbitrary")
mcols(gr_repliseq) <- mcols(repliseq)[ovl, ]
names(mcols(gr_repliseq)) <- paste0("repliseq_",
names(mcols(gr_repliseq)))
# Back to tibble
tib_repliseq <- as_tibble(gr_repliseq)
# Join with tib
tib <- full_join(tib_padamid_combined, tib_repliseq)## Joining, by = c("seqnames", "start", "end")# Plot
PlotScatterBinned(tib, "RPE_wt_Ki67", "RPE_wt_LMNB1",
color_by = tib$repliseq_RPE.hTERT, ylimits_col = c(-4.5, 4.5))## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib, "HCT116_wt_Ki67", "HCT116_wt_LMNB1",
color_by = tib$repliseq_HCT116, ylimits_col = c(-4.5, 4.5))## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib, "K562_wt_Ki67", "K562_wt_LMNB1",
color_by = tib$repliseq_K562, ylimits_col = c(-4.5, 4.5))## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib, "K562_wt_Ki67", "K562_wt_LMNB1",
color_by = tib$repliseq_K562, ylimits_col = c(-2.5, 2.5))## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
Also, create some example tracks for the manuscript of these figures.
# Plot
PlotDataTracksLight(input_tib = tib,
exp_names = c("RPE_wt_Ki67",
"RPE_wt_LMNB1",
"repliseq_RPE.hTERT"),
centromeres,
color_groups = c(RPE_wt_Ki67 = "Ki67",
RPE_wt_LMNB1 = "LMNB1",
repliseq_RPE.hTERT = "rt"),
smooth = 9, plot_chr = "chr11", plot_end = 65e6,
fix_yaxis = F,
color_list = c(colors_set1[c(1:2)], "grey50"))## Warning: Removed 165 rows containing missing values (geom_rect).
PlotDataTracksLight(input_tib = tib,
exp_names = c("HCT116_wt_Ki67",
"HCT116_wt_LMNB1",
"repliseq_HCT116"),
centromeres,
color_groups = c(HCT116_wt_Ki67 = "Ki67",
HCT116_wt_LMNB1 = "LMNB1",
repliseq_HCT116 = "rt"),
smooth = 9, plot_chr = "chr11", plot_end = 65e6,
fix_yaxis = F,
color_list = c(colors_set1[c(1:2)], "grey50"))## Warning: Removed 142 rows containing missing values (geom_rect).
PlotDataTracksLight(input_tib = tib,
exp_names = c("K562_wt_Ki67",
"K562_wt_LMNB1",
"repliseq_K562"),
centromeres,
color_groups = c(K562_wt_Ki67 = "Ki67",
K562_wt_LMNB1 = "LMNB1",
repliseq_K562 = "rt"),
smooth = 9, plot_chr = "chr11", plot_end = 65e6,
fix_yaxis = F,
color_list = c(colors_set1[c(1:2)], "grey50"))## Warning: Removed 174 rows containing missing values (geom_rect).
5. Gene expression versus Ki67 scores
Repeat the exercise from above with gene expression scores.
# Load the genes + results - and combine
genes <- readRDS("../ts210514_RNAseq_Ki67_depletion/ts210514_rnaseq_processing/genes.rds")
tib_fpkm <- readRDS("../ts210514_RNAseq_Ki67_depletion/ts210514_rnaseq_processing/genes_fpkm.rds")
tib_fpkm <- full_join(tib_fpkm,
as_tibble(genes)) %>%
dplyr::select(seqnames, start, end, strand, gene_id, DMSO_expr, IAA_expr) %>%
mutate(start = start - 5e4,
end = end + 5e4)## Joining, by = "gene_id"# Calculate gene interaction scores
tib_combined <- as_tibble(findOverlaps(as(tib_fpkm, "GRanges"),
as(tib, "GRanges"),
ignore.strand = T)) %>%
mutate(Ki67 = tib$HCT116_AID_doublethy_Ki67[subjectHits],
LMNB1 = tib$HCT116_AID_doublethy_LMNB1[subjectHits]) %>%
group_by(queryHits) %>%
dplyr::summarise(Ki67 = mean(Ki67, na.rm = T),
LMNB1 = mean(LMNB1, na.rm = T)) %>%
ungroup() %>%
mutate(seqnames = tib_fpkm$seqnames[queryHits],
gene = tib_fpkm$gene_id[queryHits],
DMSO_expr = tib_fpkm$DMSO_expr[queryHits],
DMSO_log2 = log2(DMSO_expr + 1))
# Make simple scatter plots first
PlotScatterBinned(tib_combined, "Ki67", "DMSO_log2")## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib_combined, "LMNB1", "DMSO_log2")## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib_combined, "Ki67", "LMNB1",
color_by = tib_combined$DMSO_log2, ylimits_col = c(-0.5, 2.9),
midpoint_col = 1.2)## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
# Load the genes + results - and combine
genes <- readRDS("../ts191220_laminaVsNucleolus_NewAnalyses/ts200113_GeneExpression/genes.rds")
tib_fpkm <- readRDS("../ts191220_laminaVsNucleolus_NewAnalyses/ts200113_GeneExpression/tib_fpkm.rds")
tib_fpkm <- full_join(tib_fpkm,
as_tibble(genes)) %>%
dplyr::select(seqnames, start, end, strand, gene_id, RPE_expr, HCT116_expr, K562_expr) %>%
mutate(start = start - 5e4,
end = end + 5e4)## Joining, by = "gene_id"# Calculate gene interaction scores
tib_combined <- as_tibble(findOverlaps(as(tib_fpkm, "GRanges"),
as(tib, "GRanges"),
ignore.strand = T)) %>%
mutate(RPE_Ki67 = tib$RPE_wt_Ki67[subjectHits],
RPE_LMNB1 = tib$RPE_wt_LMNB1[subjectHits],
HCT116_Ki67 = tib$HCT116_wt_Ki67[subjectHits],
HCT116_LMNB1 = tib$HCT116_wt_LMNB1[subjectHits],
K562_Ki67 = tib$K562_wt_Ki67[subjectHits],
K562_LMNB1 = tib$K562_wt_LMNB1[subjectHits]) %>%
group_by(queryHits) %>%
dplyr::summarise(RPE_Ki67 = mean(RPE_Ki67, na.rm = T),
RPE_LMNB1 = mean(RPE_LMNB1, na.rm = T),
HCT116_Ki67 = mean(HCT116_Ki67, na.rm = T),
HCT116_LMNB1 = mean(HCT116_LMNB1, na.rm = T),
K562_Ki67 = mean(K562_Ki67, na.rm = T),
K562_LMNB1 = mean(K562_LMNB1, na.rm = T)) %>%
ungroup() %>%
mutate(seqnames = tib_fpkm$seqnames[queryHits],
gene = tib_fpkm$gene_id[queryHits],
RPE_expr = log2(tib_fpkm$RPE_expr[queryHits]+1),
HCT116_expr = log2(tib_fpkm$HCT116_expr[queryHits]+1),
K562_expr = log2(tib_fpkm$K562_expr[queryHits]+1))
# Make simple scatter plots
PlotScatterBinned(tib_combined, "RPE_Ki67", "RPE_LMNB1",
color_by = tib_combined$RPE_expr, ylimits_col = c(-0.5, 2.9),
midpoint_col = 1.2)## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib_combined, "HCT116_Ki67", "HCT116_LMNB1",
color_by = tib_combined$HCT116_expr, ylimits_col = c(-0.5, 2.9),
midpoint_col = 1.2)## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
PlotScatterBinned(tib_combined, "K562_Ki67", "K562_LMNB1",
color_by = tib_combined$K562_expr, ylimits_col = c(-0.5, 2.9),
midpoint_col = 1.2)## `summarise()` has grouped output by 'n1_bin'. You can override using the `.groups` argument.
Conclusions
Session info
sessionInfo()## R version 4.0.5 (2021-03-31)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/liblapack.so.3
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] knitr_1.33 caTools_1.18.2 corrr_0.4.3
## [4] GGally_2.1.2 RColorBrewer_1.1-2 rtracklayer_1.50.0
## [7] GenomicRanges_1.42.0 GenomeInfoDb_1.26.7 IRanges_2.24.1
## [10] S4Vectors_0.28.1 BiocGenerics_0.36.1 forcats_0.5.1
## [13] stringr_1.4.0 dplyr_1.0.7 purrr_0.3.4
## [16] readr_2.0.0 tidyr_1.1.3 tibble_3.1.3
## [19] ggplot2_3.3.5 tidyverse_1.3.1
##
## loaded via a namespace (and not attached):
## [1] bitops_1.0-7 matrixStats_0.60.0
## [3] fs_1.5.0 lubridate_1.7.10
## [5] httr_1.4.2 tools_4.0.5
## [7] backports_1.2.1 bslib_0.2.5.1
## [9] utf8_1.2.2 R6_2.5.0
## [11] DBI_1.1.1 colorspace_2.0-2
## [13] withr_2.4.2 tidyselect_1.1.1
## [15] compiler_4.0.5 cli_3.0.1
## [17] rvest_1.0.1 Biobase_2.50.0
## [19] xml2_1.3.2 DelayedArray_0.16.3
## [21] labeling_0.4.2 sass_0.4.0
## [23] scales_1.1.1 digest_0.6.27
## [25] Rsamtools_2.6.0 rmarkdown_2.9
## [27] XVector_0.30.0 pkgconfig_2.0.3
## [29] htmltools_0.5.1.1 MatrixGenerics_1.2.1
## [31] highr_0.9 dbplyr_2.1.1
## [33] rlang_0.4.11 readxl_1.3.1
## [35] rstudioapi_0.13 farver_2.1.0
## [37] jquerylib_0.1.4 generics_0.1.0
## [39] jsonlite_1.7.2 BiocParallel_1.24.1
## [41] RCurl_1.98-1.3 magrittr_2.0.1
## [43] GenomeInfoDbData_1.2.4 Matrix_1.3-4
## [45] Rcpp_1.0.7 munsell_0.5.0
## [47] fansi_0.5.0 lifecycle_1.0.0
## [49] stringi_1.7.3 yaml_2.2.1
## [51] SummarizedExperiment_1.20.0 zlibbioc_1.36.0
## [53] plyr_1.8.6 grid_4.0.5
## [55] crayon_1.4.1 lattice_0.20-44
## [57] Biostrings_2.58.0 haven_2.4.1
## [59] hms_1.1.0 pillar_1.6.2
## [61] codetools_0.2-18 reprex_2.0.0
## [63] XML_3.99-0.6 glue_1.4.2
## [65] evaluate_0.14 modelr_0.1.8
## [67] vctrs_0.3.8 tzdb_0.1.2
## [69] cellranger_1.1.0 gtable_0.3.0
## [71] reshape_0.8.8 assertthat_0.2.1
## [73] xfun_0.24 broom_0.7.9
## [75] GenomicAlignments_1.26.0 ellipsis_0.3.2